Beihang University

About: Beihang University is a education organization based out in Beijing, China. It is known for research contribution in the topics: Computer science & Control theory. The organization has 67002 authors who have published 73507 publications receiving 975691 citations. The organization is also known as: Beijing University of Aeronautics and Astronautics.

SuperAgent: A Customer Service Chatbot for E-commerce Websites

Abstract: Conventional customer service chatbots are usually based on human dialogue, yet significant issues in terms of data scale and privacy. In this paper, we present SuperAgent, a customer service chatbot that leverages large-scale and publicly available e-commerce data. Distinct from existing counterparts, SuperAgent takes advantage of data from in-page product descriptions as well as user-generated content from e-commerce websites, which is more practical and cost-effective when answering repetitive questions, freeing up human support staff to answer much higher value questions. We demonstrate SuperAgent as an add-on extension to mainstream web browsers and show its usefulness to user’s online shopping experience.

Adaptive Compensation Method for High-Speed Surface PMSM Sensorless Drives of EMF-Based Position Estimation Error

Abstract: To improve the performance of the surface permanent magnet synchronous motor drives, a sensorless control scheme based on the sliding-mode observer with an orthogonal phase-locked loop (PLL) incorporating two synchronous frequency-extract filters (SFFs) is proposed. The rotor position estimation errors are analyzed. The analysis results show that the harmonic errors of the estimated signal are hard to eliminate completely. Therefore, an improved adaptive notch filter - The SFF is proposed to extract the fundamental wave of the rotor position estimation before applying to the PLL. This method of the PLL combined SFFs can compensate the estimated back electromotive force harmonic error efficiently and adaptively. An experimental driveline system used for testing the electrical performance of the developed magnetically suspended motor is built. The effectiveness and the feasibility of the proposed method are validated with the experimental results.

A Transforming Metal Nanocomposite with Large Elastic Strain, Low Modulus, and High Strength

Abstract: Freestanding nanowires have ultrahigh elastic strain limits (4 to 7%) and yield strengths, but exploiting their intrinsic mechanical properties in bulk composites has proven to be difficult. We exploited the intrinsic mechanical properties of nanowires in a phase-transforming matrix based on the concept of elastic and transformation strain matching. By engineering the microstructure and residual stress to couple the true elasticity of Nb nanowires with the pseudoelasticity of a NiTi shape-memory alloy, we developed an in situ composite that possesses a large quasi-linear elastic strain of over 6%, a low Young's modulus of ~28 gigapascals, and a high yield strength of ~1.65 gigapascals. Our elastic strain-matching approach allows the exceptional mechanical properties of nanowires to be exploited in bulk materials.

Understanding latent interactions in online social networks

Abstract: Popular online social networks (OSNs) like Facebook and Twitter are changing the way users communicate and interact with the Internet. A deep understanding of user interactions in OSNs can provide important insights into questions of human social behavior and into the design of social platforms and applications. However, recent studies have shown that a majority of user interactions on OSNs are latent interactions, that is, passive actions, such as profile browsing, that cannot be observed by traditional measurement techniques.In this article, we seek a deeper understanding of both active and latent user interactions in OSNs. For quantifiable data on latent user interactions, we perform a detailed measurement study on Renren, the largest OSN in China with more than 220 million users to date. All friendship links in Renren are public, allowing us to exhaustively crawl a connected graph component of 42 million users and 1.66 billion social links in 2009. Renren also keeps detailed, publicly viewable visitor logs for each user profile. We capture detailed histories of profile visits over a period of 90 days for users in the Peking University Renren network and use statistics of profile visits to study issues of user profile popularity, reciprocity of profile visits, and the impact of content updates on user popularity. We find that latent interactions are much more prevalent and frequent than active events, are nonreciprocal in nature, and that profile popularity is correlated with page views of content rather than with quantity of content updates. Finally, we construct latent interaction graphs as models of user browsing behavior and compare their structural properties, evolution, community structure, and mixing times against those of both active interaction graphs and social graphs.